1st commit

app.py

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+import numpy as np
+import pandas as pd
+import requests
+import os
+import gradio as gr
+import json
+from dotenv import load_dotenv, find_dotenv
+_ = load_dotenv(find_dotenv())
+
+from predibase import Predibase, FinetuningConfig, DeploymentConfig
+
+# Get a KEY from https://app.predibase.com/
+api_token  = os.getenv('PREDIBASE_API_KEY')
+pb = Predibase(api_token=api_token)
+
+adapter_id = 'tour-assistant-model/14'
+lorax_client = pb.deployments.client("solar-1-mini-chat-240612")
+
+
+def extract_json(gen_text, n_shot_learning=0):
+    if(n_shot_learning == -1) :
+        start_index = 0
+    else :
+        start_index = gen_text.index("### Response:\n{") + 14
+    if(n_shot_learning > 0) :
+        for i in range(0, n_shot_learning):
+            gen_text = gen_text[start_index:]
+            start_index = gen_text.index("### Response:\n{") + 14
+    end_index = gen_text.find("}\n\n### ") + 1
+    return gen_text[start_index:end_index]
+
+def get_completion(prompt):
+    return lorax_client.generate(prompt, adapter_id=adapter_id, max_new_tokens=1000).generated_text
+
+def greet(input):
+	total_prompt=f"""
+<|im_start|>system\nYou are a helpful support assistant. Answer the following question.<|im_end|>
+<|im_start|>question\n How much are union dues, and what do they cover?
+<|im_start|>answer\nThe union dues for our union is 3%."<|im_end|>
+
+<|im_start|>system\nYou are a helpful support assistant. Answer the following question.<|im_end|>
+<|im_start|>question
+{input}. Return as a JSON response<|im_end|>
+<|im_start|>answer
+"""
+
+	print("***total_prompt:")
+	print(total_prompt)
+	response = get_completion(total_prompt)
+	#gen_text = response["predictions"][0]["generated_text"]
+	#return json.dumps(extract_json(gen_text, 3))
+
+	###gen_text = response["choices"][0]["text"]
+
+	#return gen_text
+
+	###return json.dumps(extract_json(gen_text, -1))
+	return response
+
+	#return json.dumps(response)
+
+#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+#iface.launch()
+
+#iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Text to find entities", lines=2)], outputs=[gr.HighlightedText(label="Text with entities")], title="NER with dslim/bert-base-NER", description="Find entities using the `dslim/bert-base-NER` model under the hood!", allow_flagging="never", examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
+iface = gr.Interface(fn=greet, inputs=[gr.Textbox(label="Question", lines=3)], outputs="json")
+iface.launch()

requirements.txt

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+openai
+python-dotenv
+predibase